Trend based distribution parameter suggestion

ABSTRACT

Methods, systems, and apparatus, including computer programs encoded on a computer storage medium, for distributing content are disclosed. In one aspect, a method includes accessing data specifying a plurality of search queries. Content distribution campaigns (“campaigns”) in which distribution of at least one content item is conditioned on a distribution parameter matching one of the search queries are identified. Two or more similar campaigns are identified, and a search query that matches a distribution parameter in at least one of the similar campaigns is identified as a candidate content distribution parameter. A trend score for the candidate content distribution parameter is determined based on a change in a submission rate of search queries that match the candidate distribution parameter. Suggestion data suggesting the candidate content distribution parameter as an additional content distribution parameter for at least one of the similar campaigns is provided based on the trend score.

CROSS-REFERENCE TO RELATED APPLICATION

This application is a continuation of and claims priority to U.S. patentapplication Ser. No. 14/159,671 entitled “Trend Based DistributionParameter Suggestion” filed on Jan. 24, 2014, the entire contents ofwhich is incorporated by reference for all purposes.

BACKGROUND

This specification relates to data processing and content distribution.

The Internet provides access to a wide variety of resources. Forexample, video and/or audio files, as well as web pages for particularsubjects or that present particular news articles are accessible overthe Internet. To identify resources that may satisfy a user'sinformational need, the user can submit a search query to a searchsystem and receive a search results page that identifies one or moreresources. The search results page can include “slots” (i.e., specifiedportions of the web page) in which advertisements (or other contentitems) can be presented. Advertisements or other content items that arepresented in the slots are selected for presentation by a contentdistribution system that can perform an auction as part of the selectionprocess.

SUMMARY

In general, one innovative aspect of the subject matter described inthis specification can be embodied in methods that include the actionsof accessing data specifying a plurality of search queries; identifying,based on at least some of the search queries, content distributioncampaigns in which distribution of at least one content item isconditioned on a distribution parameter matching one of the searchqueries; identifying two or more of the content distribution campaignsthat have at least a specified level of similarity to each other;identifying, as a set of candidate distribution parameters and from theplurality of submitted search queries, a set of search queries thatmatch a distribution parameter in at least one of the identified two ormore content distribution campaigns having at least the specified levelof similarity to each other; determining, for each candidatedistribution parameter in the set, a trend score based on a change in asubmission rate of the set of search queries that match the candidatedistribution parameter; and providing, based on the trend score,suggestion data suggesting at least one of the candidate distributionparameters as an additional distribution parameter for at least one ofthe two or more content distribution campaigns.

These and other embodiments can each optionally include one or more ofthe following features. Identifying content distribution campaigns inwhich distribution of at least one content item is conditioned on adistribution parameter matching one of the search queries can includeidentifying at least one content distribution campaign in which a broadmatch distribution parameter differs from one or more of the searchqueries that match the broad match distribution parameter. Providingsuggestion data suggesting at least one of the candidate distributionparameters can include providing suggestion data suggesting at least oneof the one or more search queries that match the broad matchdistribution parameter as an additional distribution parameter for theat least one content distribution campaign.

Determining a trend score can include determining, for a particularcandidate distribution parameter, a first number of users that submitteda search query matching the candidate distribution parameter over afirst time period; determining, for the particular candidatedistribution parameter, a second number of users that submitted a searchquery matching the candidate distribution parameter that is matched by aparticular search query over a second time period that differs from thefirst time period and occurs after the first time period; anddetermining the trend score based on a difference between the firstnumber of users and the second number of users. Determining the trendscore can include determining that the trend score is an increasingtrend score based on the second number being greater than the firstnumber.

Methods can include the actions of determining that a magnitude of thetrend score meets a trend threshold, wherein providing suggestion datasuggesting at least one of the candidate distribution parameters as anadditional distribution parameter can include providing suggestion datasuggesting the particular candidate distribution parameter as anadditional distribution parameter based on the magnitude of the trendscore meeting the trend threshold.

Identifying two or more of the content distribution campaigns that haveat least the specified level of similarity to each other can includeidentifying two or more of the content distribution campaigns that haveat least a specified percentage of matching distribution parameters; anddetermining that a difference between a number of impressions allocatedto each of the two or more content distribution campaigns is within aspecified difference value; and providing suggestion data suggesting atleast one of the candidate distribution parameters as the additionaldistribution parameter can include providing suggestion data suggestingthe at least one candidate distribution parameter to each of the two ormore content distribution parameters based on the identification of thetwo or more content distribution campaigns having at least the specifiedlevel of similarity to each other.

Particular embodiments of the subject matter described in thisspecification can be implemented so as to realize one or more of thefollowing advantages. Content sponsors can be provided suggesteddistribution parameters based on changing user interest in varioussearch queries (as indicated by changes in search query volume), therebyenabling the content sponsors to distribute content items based on thechanging user interests. Users are provided with content items that aremore relevant to their search queries by associating newly popularsearch queries with content distribution campaigns that distributecontent items related to the newly popular search queries.

The details of one or more embodiments of the subject matter describedin this specification are set forth in the accompanying drawings and thedescription below. Other features, aspects, and advantages of thesubject matter will become apparent from the description, the drawings,and the claims.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 is a block diagram of an example environment in which contentdistribution system distributes content to user devices.

FIG. 2A is an example data flow for suggesting distribution keywords tocontent sponsors.

FIG. 2B is an illustration of an example user interface for presentingsearch query volume information.

FIG. 3 is a flow chart of an example process for suggesting distributionkeywords to content sponsors.

FIG. 4 is block diagram of an example computer system.

Like reference numbers and designations in the various drawings indicatelike elements.

DETAILED DESCRIPTION

Content distribution parameters are suggested to content sponsors (e.g.,advertisers) based on changes in search query volume. For example, whensearch query volume for a particular search query (or set of searchqueries) increases at least a specified amount, that particular searchquery can be suggested to one or more content sponsors as an additionalcontent distribution parameter that is used to condition presentation ofcontent items provided by the content sponsor.

For example, assume that Toy Company 1 uses the phrase “toy truck” as acontent distribution parameter for one of its advertisements, and that abroad match type is selected for the phrase toy truck. The selection ofa broad match type for the phrase “toy truck” is an indication thatthere need not be an exact match between search queries and the phrase“toy truck” in order for the advertisement to be eligible forpresentation. Continuing with this example, assume that “toy truck” ismatched by the search query “red toy truck,” and that the search queryvolume for the search query “red toy truck” has increased at least aspecified amount relative to a previous period of time (e.g., relativeto a previous month or previous year). In some implementations, theincrease in the search query volume can result in the search query “redtoy truck” being suggested to Toy Company 1 as an additional contentdistribution parameter because Toy Company 1 uses a content distributionparameter that is matched by the search query associate with theincreased search query volume.

As discussed in more detail below, other content sponsors to which theparticular search query is suggested as an additional contentdistribution parameter can be selected, at least based on part, on alevel of similarity between the other content sponsors and contentsponsors that already use a content distribution parameter that ismatched by the particular search query. For example, the additionalcontent distribution parameter can be suggested to other contentsponsors that use content distribution parameters that are similar tothose used by Toy Company 1 and/or are otherwise identified as beingsimilar to Toy Company 1 (e.g., in a same industry as Toy Company 1).

The search query volume for a particular query can be determined on anaggregate basis so that the search query volume does not include useridentifiable information. In some implementations, search query volumeinformation is only provided and/or used for search queries that havebeen received at least a minimum number of times to facilitate use ofsearch queries that do not include user identifiable information.

In situations in which the systems discussed here collect personalinformation about users, or may make use of personal information, theusers may be provided with an opportunity to control whether programs orfeatures collect user information (e.g., information about a user'ssocial network, social actions or activities, profession, a user'spreferences, or a user's current location), or to control whether and/orhow to receive content from the content sen.er that may be more relevantto the user. In addition, certain data may be treated in one or moreways before it is stored or used, so that personally identifiableinformation is removed. For example, a user's identity may be treated sothat no personally identifiable information can be determined for theuser, or a user's geographic location may be generalized where locationinformation is obtained (such as to a city, ZIP code, or state level),so that a particular location of a user cannot be determined. Thus, theuser may haw control over how information is collected about the userand used by a content server.

For purposes of example, the description that follows refers toadvertisements as the content items that are distributed to user devicesand advertisers as the content sponsors that provide the advertisements.The description is also applicable to distribution of other types ofcontent items (e.g., audio files, video files, or other content) thatare provided to user devices.

FIG. 1 is a block diagram of an example environment 100 in which contentdistribution system 110 distributes content to user devices 106. Theexample environment 100 includes a network 102 such as a local areanetwork (LAN), wide area network (WAN), the Internet, or a combinationthereof. The network 102 connects websites 104, user devices 106,advertisers 108, and the advertisement management system 110. Theexample environment 100 may include any number of websites 104, userdevices 106, and advertisers 108.

A website 104 is one or more resources 105 associated with a domain nameand hosted by one or more servers. An example website is a collection ofweb pages formatted in hypertext markup language (HTML) that can containtext, images, multimedia content, and programming elements, e.g.,scripts. Each website 104 is maintained by a publisher, e.g., an entitythat manages and/or owns the website 104.

A resource 105 is data provided by the website 104 over the network 102and that is associated with a resource address. Resources include HTMLpages, word processing documents, and portable document format (PDF)documents, images, video, and feed sources, to name only a few. Theresources can include content, e.g., words, phrases, images and soundsthat may include embedded information (such as meta-information inhyperlinks) and/or embedded instructions (such as scripts).

A user device 106 is an electronic device that is under control of auser and is capable of requesting and receiving resources over thenetwork 102. Example user devices 106 include personal computers, mobilecommunication devices, and other devices that can send and receive dataover the network 102. A user device 106 typically includes a userapplication, such as a web browser, to facilitate the sending andreceiving of data over the network 102.

A user device 106 can request resources 105 from a website 104. In turn,data representing the resource 105 can be provided to the user device106 for presentation by the user device 106. The data representing theresource 105 can also include data specifying a portion of the resourceor a portion of a user display (e.g., a presentation location of apop-up window or in a slot of a web page) in which advertisements can bepresented. These specified portions of the resource or user display arereferred to as advertisement slots.

To facilitate searching of these resources, the environment can includea search system 112 that identifies the resources by crawling andindexing the resources provided by the publishers on the websites 104.Data about the resources can be indexed based on the resource to whichthe data corresponds. The indexed and, optionally, cached copies of theresources are stored in an indexed cache 114.

User devices 106 can submit search queries 116 to the search system 112over the network 102. In response, the search system 112 accesses theindexed cache 114 to identify resources that are relevant to the searchquery 116 (e.g., have at least a threshold relevance score with respectto the search query). The search system 112 identifies the resources inthe form of search results 118 and returns the search results 118 to theuser devices 106 in search results pages 119.

A search result 118 is data generated by the search system 112 thatidentifies a resource that is responsive to a particular search query,and includes a link to the resource. An example search result 118 caninclude a web page title, a snippet of text or a portion of an imageextracted from the web page, and the URL of the web page. Search resultspages 119 can also include one or more advertisement slots 120 in whichadvertisements can be presented. The advertisement slots 120 can alsofacilitate presentation of other content items instead of or in additionto, advertisements.

When search results 118 are requested by a user device 106, the contentdistribution system 110 receives an advertisement request (or anothercontent item request) requesting advertisements (or another contentitem) to be provided with the search results 118. The advertisementrequest can include characteristics of the advertisement slots 120 thatare defined for the search results page 119. For example, a size of theadvertisement slot 120, and/or media types that are eligible forpresentation in the advertisement slot 120 can be provided to thecontent distribution system 110. Similarly, data specifying one or moreterms of the search query 116 in response to which the search resultspage 119 is being provided can also be included in the advertisementrequest to facilitate identification of advertisements that are relevantto the search query 116.

Based on data included in the advertisement request, the contentdistribution system 110 selects advertisements that are eligible to beprovided in response to the advertisement request (“eligibleadvertisements”). Eligible advertisements can include, for example,advertisements having characteristics that match the characteristics ofthe advertisement slots 118 and that are identified as relevant to thesearch query 116.

In some implementations, advertisements that are selected as eligibleadvertisements by the content distribution system 110 are thoseadvertisements having distribution parameters (i.e., data with whichdistribution of the advertisement is managed) that match the searchquery 116 and/or other information included in the advertisementrequest. The advertisement management system 110 can select, from theset of eligible advertisements, one or more advertisements forpresentation with the search results page 119. Each advertisement can beselected for presentation based, at least in part, on how well adistribution keyword (also referred to as a keyword) for theadvertisement matches the search query and/or on the outcome of anauction.

A distribution keyword, which is a type of distribution parameter, canmatch a search query by having the same textual content (“text”) as thesearch query. For example, an advertisement (or another content item)associated with the distribution keyword “basketball” can be selectedfor presentation with a search results page that is provided in responseto the search query “basketball,” since the search query and thedistribution keyword are exactly the same. This is referred to as anexact match.

A distribution keyword can also match a search query by having text thatis identified as being sufficiently relevant, or sufficiently similar,to the search query despite having different text than the search query.For example, an advertisement (or another content item) associated withthe distribution keyword “basketball” may also be selected forpresentation with a search results page that is provided in response tothe search query “sports” because basketball is a type of sport, and,therefore, is relevant to the term “sports.” This type of match isreferred to as a broad match.

For purposes of this document, a distribution keyword can be consideredto match a search query (or vice versa) when a measure of similarity(e.g., semantic or topical similarity) between the distribution keywordand the search query meets a specified threshold value. The measure ofsimilarity can be specified based on a cosine distance between theattributes of the search query and the attributes of the distributionkeyword, an edit distance between the search query and the distributionkeyword, user feedback specifying a measure of similarity between thesearch query and the distribution keyword, or another indication ofsimilarity between the search query and the distribution keyword (e.g.,each of the search query and the distribution keyword being categorizedto a same topic in a topical hierarchy).

The content distribution system 110 can also select advertisements forpresentation in advertisement slots 120 of a search results page 119based on results of an auction. For example, the content distributionsystem 110 can receive bids from advertisers and allocate theadvertisement slots to the highest bidders at the conclusion of theauction. The bids are amounts that the advertisers are willing to payfor presentation (or selection) of their advertisement with a searchresults page. For example, a bid can specify an amount that anadvertiser is willing to pay for each 1000 impressions (e.g.,presentations) of the advertisement, referred to as a CPM bid.Alternatively, the bid can specify an amount that the advertiser iswilling to pay for a user interaction with (e.g., a click-through of orhovering a pointer over) the advertisement or a “conversion” followinguser interaction with the advertisement. A conversion occurs when a userconsummates a transaction related to an advertisement being providedwith a search results page. What constitutes a conversion may vary fromcase to case and can be determined in a variety of ways.

Many different types of auctions can be used to select theadvertisements (or other content items) for presentation. First priceauctions, generalized second price (GSP) auctions andVickery-Clarke-Groves auctions are a few examples of auctions that canbe performed by the content distribution system 110 to selectadvertisements (or other content items) for presentation.

An advertiser 108 can select any number of distribution keywords toassociate with a particular advertisement (or group of advertisements).However, the time required to manage a set of distribution keywordsincreases as the number of distribution keywords in the set increasesand as the number of advertisements, or groups of advertisements, thatare associated with different sets of distribution keywords increases.Additionally, it can be difficult for an advertiser 108 to identify orpredict the specific search queries that will be popular at any giventime, particularly since user interest in various topics can quicklychange. As such, the advertiser 108 may miss out on an opportunity tohave its advertisements presented to users submitting recently popularsearch queries.

The environment 100 includes a suggestion apparatus 122 that suggestsdistribution parameters for advertisements (or other content items)based on changes in search query volume for search queries that arerelated to the advertisements. The suggestion apparatus 122 obtainssearch query trend data 124 that specify, for each of a plurality ofsearch queries, a search query volume indicative of a number of usersthat have submitted the search query to the search system 112. In someimplementations, the search query volume for a particular search querycan specify a number of different users that have submitted theparticular search query. In some implementations, the search queryvolume for the particular search query can specify a total number oftimes that the particular search query has been submitted (e.g., over aspecified period), irrespective of whether a same user submitted theparticular search query multiple times.

The search query volume for a particular search query is an indicationof user interest in topics to which the particular search query isrelated. For example, user interest in topics or information related toa particular search query is considered to increase as the query volumefor the particular search query increases. For brevity, user interest intopics or information related to a particular search query is referredto as user interest in the search query.

The search query volume can be tracked over specified periods, orintervals, (e.g., 1 hour, 1 day, 1 week, 1 month, or 1 year) todetermine whether user interest in the search query over a current (ormost recent) period has increased or decreased relative to user interestin the search query over one or more previous periods. For example,assume that over one particular day 750,000 users submitted a particularsearch query to the search system 112. Further assume that on asubsequent day 1,500,000 users submitted a particular search query tothe search system 112. In this example, the search query volume for theparticular search query increased by 100%, such that user interest inthe particular search query is considered to have increased by 100% fromthe particular day to the subsequent day. Therefore, user interest inthe particular search query is considered to have trended up from theparticular day to the subsequent day, and this upward trend for theparticular search query can be specified in the search query trend datafor this particular search query.

In some implementations, the query volume for a particular search queryduring various time periods can be used to generate a trend score forthe particular search query. The trend score can be based, for example,on a difference between the query volume for a most recent time period(e.g., a most recent week or month) and a previous corresponding timeperiod (e.g., a previous week or month). Continuing with the exampleabove, the trend score for the particular query can be a number greaterthan 1.0 (or some other value) indicating that the search query volumefor the particular search query is increasing (e.g., rising from 750,000on the particular day to 1,500,000 on the subsequent day). Examples fordetermining a trend score are provided below with respect to FIG. 2A.

As described in more detail below, the suggestion apparatus 122 monitorstrend scores for various search queries, and suggests, as additionaldistribution keywords, search queries having trend scores that meet atrend score threshold. For example, assume that the particular searchquery in the example above is “red toy truck,” and that a particular toyadvertiser does not have “red toy truck” specified as a distributionkeyword for its advertisements. In this example, in response todetermining that the trend score for “red toy truck” meets the trendscore threshold, the suggestion apparatus 122 can suggest “red toytruck” as an additional distribution keyword for the particular toyadvertiser. The selection of search queries to be suggested asadditional distribution keywords and the identification of advertisers(or other content sponsors) to whom the suggestions are made aredescribed in more detail below.

FIG. 2A is an example data flow 200 for suggesting distribution keywordsto content sponsors (e.g., advertisers). The data flow 200 begins withthe suggestion apparatus 122 obtaining search query data 202. The searchquery data 202 can include, for example, data specifying a plurality ofsearch queries (e.g., SQ1, SQ2, . . . , SQn) that have been submitted byusers. The search query data 202 can also include search query volumedata (e.g., SQVD1, SQVD2, . . . , SQTVDn) for the search queries. Thesearch query volume data can specify, for each search query, a searchquery volume (e.g., a number of times that the corresponding searchquery was received from user devices). For example, SQVD1 can specify anumber of times that the search query SQ1 was received from userdevices.

In some implementations, the search query volume data can specify searchquery volumes corresponding to multiple different periods of time. Forexample, the search query volume data SQVD1 can delineate the searchquery volume for SQ1 on a weekly basis, monthly basis, and/or yearlybasis. Similarly, the search query volume data SQVS2 can delineate thesearch query volume for SQ2 on a weekly basis, monthly basis, and/oryearly basis.

The suggestion apparatus 122 uses the search query data 202 to identifya set of content distribution campaigns 204 (e.g., CDC1, CDC2, . . . ,CDCx) that have distribution keywords that are matched by at least oneof the search queries. As discussed above, a match between distributionkeywords and search queries can be determined to exist even when thedistribution keywords and search queries differ. For example, when adistribution keyword is associated with a broad match type, thedistribution keyword can be matched by search queries that have at leasta specified level of similarity to the distribution keyword.

The suggestion apparatus 122 identifies a set of similar contentdistribution campaigns 206 based, at least in part, on the distributionkeywords (e.g., DK1, DK2, . . . DKx) that are included in each of thecontent distribution campaigns (e.g., CDC1, CDC2, . . . , CDCx). In someimplementations, the suggestion apparatus 122 determines a similarityscore between two content distribution campaigns based on how muchoverlap there is between the distribution keywords that are included inthe two content distribution campaigns. For example, the suggestionapparatus can compare the distribution keywords that are included inCDC1 with the distribution keywords that are included in CDC2 todetermine a portion of the distribution keyword that are included inboth CDC1 and CDC2. If the portion of distribution keyword included inboth of CDC1 and CDC2 meets a specified amount (e.g., specifiedpercentage), the content distribution campaigns can be consideredsimilar, and therefore included in the set of content distributioncampaigns.

In some implementations, each set of similar content distributioncampaigns only includes content distribution campaigns that areassociated with a similar level of content item performance. Forexample, the number of impressions that are allocated to each of thecontent item distribution campaigns in the set can be required to bewithin a specified amount (e.g., within a specified number orpercentage) of other content distribution campaigns in the set. In aparticular example, one set of similar content distribution campaignscan be created for content distribution campaigns that receive fewerthan a first specified number of impressions (e.g., per week or permonth), a second set of similar content distribution campaigns can becreated for content distribution campaigns that receive at least thefirst specified number of impressions, but fewer than a second specifiednumber of impressions (e.g., per week or per month), and a third set ofsimilar content distribution campaigns can be created for contentdistribution campaigns that receive at least the second specified numberof impressions. For purposes of example, the following discussion willrefer to one set of similar campaigns 206 that includes CDC1 and CDC2.

The suggestion apparatus 122 identifies, from the search query data, aset of search queries 208 (e.g., SQ1 and SQ2) that match a distributionkeyword in at least one of the content distribution campaigns from theset of similar content distribution campaigns 206. The match can be anexact match or a broad match. The set of search queries 208 can beconsidered candidate distribution keywords for the content distributioncampaigns (e.g., CDC1 and CDC2) that are included in the set of similarcontent distribution campaigns 206.

The suggestion apparatus 122 determines a set of trend scores 210 forthe set of search queries 208. The set of trend scores 210 can include aseparate trend score for each of the search queries in the set of searchqueries 208. For example, the suggestion apparatus can determine thetrend score TS1 for SQ1 and determine the trend score TS2 for SQ2.

In some implementations, the trend score for each search query is basedon a difference between a current search query volume for the searchquery relative to a historical search query volume for the search query.For example, for a particular search query, the suggestion apparatus 122can compare the search query volume for a most recent week to a searchquery volume for a week preceding the most recent week. If the searchquery volume has increased from the preceding week to the most recentweek, the suggestion apparatus 122 can give the particular search querya trend score indicating that the search query volume for the particularsearch query has increased, and is therefore more popular. If the searchquery volume has decreased from the preceding week to the most recentweek, the suggestion apparatus 122 can give the particular search querya trend score indicating that the search query volume for the particularsearch query has decreased, and is therefore less popular that it was.

In some implementations, the trend score for a particular search querycan be determined based on the search query volume corresponding tomultiple different periods of time. For example, the trend score can bebased on a combination of a week to week change in search query volume,a month to month change in search query volume, and/or a year to yearchange in search query volume for the particular search query. In aparticular example, the trend score can be a weighted combination of theweek to week change, the month to month change, and the year to yearchange in search query volume. In some implementations, relationship 1is used to determine the trend score for a particular search query.TS_(i)=ABS(WWΔ_(i))*(w₁*WWΔ_(i)+w₂*MMΔ_(i)+w₃*YYΔ_(i))  (1)

where,

TS_(i) is the trend score for search query (i);

ABS(WWΔ_(i)) is the absolute value of the week to week change in searchquery volume for the search query (i);

w₁ is a weight value for the week to week change in search query volume;

WWΔ_(i) is the week to week change in search query volume for the searchquery (i);

w₂ is a weight value for the month to month change in search queryvolume;

MMΔ_(i) is the month to month change in search query volume for thesearch query (i);

w₃ is a weight value for the year to year change in search query volume;and

YYΔ_(i) is the year to year change in search query volume for the searchquery (i).

In some implementations, the week to week change in search query volumeis a more important indicator regarding the current user interest in asearch query than the month to month and/or year to year changes.Therefore, the weight (w₁) applied to the week to week change can behigher than the weight (w₂) applied to the month to month change and theweight (w₃) applied to the year to year change in search query volume.In a particular example, the weight w₁ can be set to 0.70, the weight w₂can be set to 0.20 and the weight w₃ can be set to 0.10, but othervalues can be used.

The suggestion apparatus 122 selects at least one of the search queriesin the set of search queries 208 for inclusion in a set of suggesteddistribution keywords 212 (“DK_Suggest”). In some implementations, theset of suggested distribution keywords 212 includes those search querieshaving a trend score that meets a trend threshold. For example, thetrend threshold can be set so that only search queries having trendscores corresponding to an increase in search query volume are selectedfor inclusion in the set of suggested distribution keywords 212. In someimplementations, the trend threshold can be set so that only searchqueries that have experienced at least a specified increase (e.g., 10%,20%, 30%, or some other increase) in search query volume (e.g., asspecified by the trend scores) are included in the set of suggesteddistribution keywords 212.

The suggestion apparatus 122 provides suggest data. 214 to one or moreof the content sponsors (e.g., 216 and 218) associated with the set ofsimilar content distribution campaigns 206. In some implementations, thesuggest data 214 specifies, for each of the content sponsors, one ormore distribution keywords from the set of suggested distributionkeywords 212. In some implementations, the suggest data 214 provided toeach content sponsor 216 and 218 identifies the full set of suggesteddistribution keywords 212.

In some implementations, the suggested distribution keywords specifiedby the suggest data provided to at least one content sponsor (e.g.,content sponsor 1 216) differs from the suggested distribution keywordsspecified by the suggest data provided to a different content sponsor(e.g., content sponsor 2 218). For example, the suggest data provided toeach different content sponsor can specify only those distributionkeywords from the set of suggested distribution keywords 212 that arenot already included in the content distribution campaign associatedwith the content sponsor.

For purposes of illustration, assume that the distribution keywords forcontent sponsor 1 216 include “distribution keyword 1” and “distributionkeyword 2” and that the content distribution keywords for contentsponsor 2 218 include “distribution keyword 3” and “distribution keyword4”. For purposes of this example, further assume that the set ofsuggested keywords 212 include “distribution keyword 1,” “distributionkeyword 4,” and “distribution keyword 5.” In this example, the suggestdata provided to content sponsor 1 216 can include data suggesting that“distribution keyword 4” and “distribution keyword 5” be added to thecontent distribution campaign associated with content sponsor 1 216,because these distribution keywords are not yet included in the contentdistribution campaign, but not suggest adding distribution keyword 1because this distribution keyword is already included in the contentdistribution campaign. Similarly, the suggest data 214 provided tocontent sponsor 2 218 can include data suggesting that “distributionkeyword 1” and “distribution keyword 5” be added to the contentdistribution campaign associated with content sponsor 2 218 becausethese distribution keywords are not yet included in the contentdistribution campaign, but not suggest adding “distribution keyword 4”because this distribution keyword is already included in the contentdistribution campaign.

In some implementations, the suggest data 214 provided to each contentsponsor can include distribution parameter change data suggesting otherchanges to the content distribution campaign (e.g., other than includingadditional distribution keywords in the content distribution campaign).For example, the suggest data 214 provided to content sponsor 1 216 mayspecify that “distribution keyword 1” has an increasing query trend(e.g., an increase in search query volume as indicated by the trendscore), and suggest that the content sponsor 1 216 increase a bidassociated with distribution keyword 1.

Alternatively, or additionally, the suggest data 214 provided to contentsponsor 1 216 can suggest a change to a match type associated with thedistribution keyword 1. Continuing with the example above in which“distribution keyword 1” has an increasing query trend, the suggest data214 provided to content sponsor 1 216 can suggest that content sponsor 1216 change a match type associated with “distribution keyword 1” frombroad match to exact match (or vice versa).

In some implementations, the suggest data 214 can also specifydistribution keywords associated with the content sponsor's contentdistribution account that have a decreasing trend (e.g., a decrease insearch query volume as indicated by the trend score), and suggest thatthe content sponsor remove the distribution keyword from the contentdistribution campaign, lower a bid associated with the contentdistribution keyword, and/or change a match type associated with thecontent distribution keyword.

In some implementations, the suggestion apparatus can provide a contentsponsor trend information related to a geography and/or terms specifiedby the content sponsor. FIG. 2B is an illustration of an example userinterface 250 through which a content sponsor can request trendinformation and trend information can be presented to the contentsponsor. The user interface 250 includes a graph portion 252 in whichtrend information can be presented to the content sponsor. For example,the graph portion 252 can present historical search query volume for oneor more different search queries or topics. In FIG. 2A, the graphportion is presenting the search query volume over multiple differenttime periods for search queries that match keyword 1 (as indicated bythe check mark in the box 254).

In some implementations, a content sponsor can request search queryvolume information for particular topics by entering one or more termsin the entry element 256 and interacting with (e.g., clicking) thesubmission element (“go”) 258. Using the one or more terms, thesuggestion apparatus 122 can identify search queries that match the oneor more terms, and provide the search query volume information for thosesearch queries in the graph portion 252. The search query volumeinformation for the identified search queries can be provided with (orwithout) the historical search query volume for keyword 1.

The content sponsor can also specify one or more particular geographiclocations that can be used to collect the search query volumeinformation for the submitted one or more terms (or any of Keyword 1,Keyword 2, or Keyword 3). For example, the content sponsor can interactwith the geo element 260 to cause presentation of a drop-down list ofgeographic regions, and the content sponsor can select one or more ofthe geographic regions for which the content sponsor wants to obtainsearch query volume information. The suggestion apparatus 122 can usethe selected geographic regions to identify search query volumeinformation corresponding to the selected geographic regions, andprovide search query volume information corresponding to the selectedgeographic regions. For example, if the content sponsor selected thegeographic region “USA” and specified the term “toy,” the suggestapparatus could identify the search query volume originating in USA forthe search query “toy,” and provide that search query volume informationin the graph portion 252.

The user interface 250 can also provide the content sponsor with asuggested keyword 262 based on the search query volume information. Forexample, the user interface 250 can present a suggested keyword alongwith an indication that the keyword is suggested for use by the contentsponsor (e.g., by way of the “recommended” notification). The searchquery volume information corresponding to the suggested keyword can bepresented in the graph portion 252, for example, through interactionwith the box 264.

FIG. 3 is a flow chart of an example process 300 for suggestingdistribution keywords to content sponsors. The process 300 can beperformed, for example, by the suggestion apparatus 122, the contentdistribution system 110, or another data processing apparatus. Theprocess 300 can also be implemented as instructions stored on computerstorage medium, and execution of the instructions by one or more dataprocessing apparatus cause the one or more data processing apparatus toperform some or all of the operations of the process 300.

Data specifying search queries that were submitted by users is accessed(302). In some implementations, the data can specify, for each searchquery, a search query volume corresponding to the search query. Forexample, the data can specify a number of users that have submitted thesearch query over one or more specified periods. In a particularexample, the data can specify a number of users that have submitted thequery over a most recent week, a previous week (e.g., a week prior tothe most recent week), a most recent month, a previous month (e.g., amonth prior to the most recent month), a most recent year, and/or aprevious year (e.g., a year prior to the most recent year). The data canalso specify a number of users that have submitted the query overdifferent periods of time than those listed here.

In some implementations, the search query volume is based on a totalnumber of different users that submitted the query. In someimplementations, the search query volume is based on a total number oftimes that the search query was submitted, irrespective of whether thequery was submitted multiple times by a same user or user device.

Content distribution campaigns are identified based on the searchqueries (304). In some implementations, the identification of contentdistribution campaigns includes identifying content distributioncampaigns in which distribution of at least one content item isconditioned on a distribution parameter matching one of the searchqueries.

For purposes of illustration, assume that the search query “toy truck”is included in the search queries specified by the obtained data. Inthis example, the search query “toy truck” can be used to identify thecontent distribution campaigns. For example, the query “toy truck” canbe used to identify content distribution campaigns having content itemsthat are eligible for distribution with search results pages provided inresponse to the search query “toy truck.” The content items that areeligible for distribution with search results pages provided in responseto the search query “toy truck” can include content items that areassociated with the distribution keyword “toy truck,” irrespective ofthe match type associated with the distribution keyword “toy truck.”

In some implementations, the identification of content distributioncampaigns can include the identification of at least one contentdistribution campaign in which a broad match distribution parameter(e.g., a broad match distribution keyword) that differs from the searchqueries (e.g., is not included in the search queries) that match thebroad match distribution parameter. Continuing with the example above,other content items that are eligible for distribution based on thesearch query “toy truck” may include those content items associated withdistribution parameters that are matched by the search query “toytruck.” For example, assume that a content item is associated with thedistribution keyword “play truck.” In this example, the distributionkeyword “play truck” may be matched by the search query “toy truck,” forexample if the distribution keyword “play truck” is specified as a broadmatch distribution keyword. Thus, in this example, the contentdistribution campaign that includes the distribution keyword “playtruck” can be one of the identified content distribution campaigns.

Two or more of the content distribution campaigns that have at least aspecified level of similarity to each other are identified (308). Insome implementations, the identification of two or more similar contentdistribution campaigns is based, at least in part, on a level ofsimilarity between the distribution keyword that are included in each ofthe content distribution campaigns. For example, content distributioncampaigns that include more of the same distribution keywords areconsidered more similar than content distribution campaigns that includefewer of the same distribution keywords.

In some implementations, the identification of two content distributioncampaigns as having at least the specified level of similarity caninclude identification of two content distribution campaigns having aspecified portion (e.g., number or percentage) of matching distributionparameters (e.g., matching keywords). The identification of two contentdistribution campaigns as similar content distribution campaigns can bebased, for example, on a percentage (or absolute number) of thedistribution keywords that are shared by the two content distributioncampaigns. For example, assume that content distribution campaign 1includes “distribution keyword 1,” “distribution keyword 2,” and“distribution keyword 3,” and that content distribution campaign 2includes “distribution keyword 1,” “distribution keyword 2,” and“distribution keyword 4.” In this example, content distribution campaign1 and content distribution campaign 2 share two out of the three (i.e.,66.67%) of the content distribution keywords. Therefore, contentdistribution campaign 1 and content distribution campaign 2 can beidentified as similar content distribution campaigns if the specifiedportion of matching distribution parameters is less than 66.67%.

In some implementations, the identification of two content distributioncampaigns as having the specified level of similarity to each other canalso require a determination that a difference in the number ofimpressions allocated to the two content distribution campaigns iswithin a specified difference value (e.g., expressed as a percentage oran absolute value). Continuing with the example above, assume thatcontent distribution campaign 1 receives 1,000,000 impressions per week(e.g., on average), and content distribution campaign 2 receives 1,000impressions per week (e.g., on average). In this example, contentdistribution campaign 1 and content distribution campaign 2 may not beidentified as having the specified level of similarity (despite theoverlap in content distribution keywords) because of the largedifference in impressions received by each of the content distributioncampaigns. If, however, content distribution campaign 2 received 900,000impressions per week (e.g., on average) and the specified differencevalue was 20%, content distribution campaign 1 and content distributioncampaign 2 would be identified as having the specified level ofsimilarity.

Example similarity criteria (e.g., matching distribution keywords and/orimpression volume) are provided for purposes of illustration.Additional, fewer, or alternative similarity criteria can be used toevaluate the similarities between content distribution campaigns. Forexample, content distribution campaigns that are associated with a samecontent sponsor can be considered more similar than content distributioncampaigns that are not associated with a same content sponsor.Similarly, content distribution campaigns that are related to a sameindustry (e.g., both related the toy industry) can be considered moresimilar than content distribution campaigns that are related todifferent industries (e.g., one campaign related to the toy industry andanother campaign related to the automobile industry).

A set of candidate distribution parameters are identified (308). In someimplementations, the set of candidate distribution parameters includethe search queries that match a distribution parameter in at least oneof the two or more content distribution campaigns. For example, eachsearch query that matches a distribution keyword in the two or morecontent distribution campaigns that are identified as having at leastthe specified level of similarity can be identified as candidatedistribution keywords for the two or more content distributioncampaigns. As discussed above, the match between the search query andthe distribution parameters of the two or more content distributioncampaigns can be based on a broad match, rather than requiring an exactmatch.

A trend score is determined for each of the candidate distributionparameters in the set (310). In some implementations, the trend scorefor a candidate distribution parameter is determined based on a changein a submission rate of the search queries matching the candidatedistribution parameter. For example, if the submission rate (e.g., therate with which users are submitting the query over a specified periodof time) of search queries that match a particular candidatedistribution keyword has increased over a most recent week, month,and/or year, the trend score for the particular candidate distributionkeyword can reflect the increased submission rate. If, however, thesubmission rate for search queries that match the particular candidatedistribution keyword has decreased over a specified period, the trendscore for the particular candidate distribution keyword can reflect thedecreased submission rate. An example relationship for computing a trendscore is discussed above with respect to FIG. 2A, but otherrelationships can also be used.

In some implementations, the trend score for a particular candidatedistribution keyword can be determined based a difference between afirst number of users that during a first period submitted a searchquery matching the particular candidate distribution keyword and asecond number of users that during a second period submitted a searchquery matching the particular candidate distribution keyword. Forexample, assume that during the first period (e.g., a particular month)a determination is made that 1,000,000 users submitted a search querymatching the particular candidate distribution keyword, and that duringa second period (e.g., a month following the particular month),1,500,000 users submitted a search query matching the particularcandidate distribution keyword. In this example, the number of usersthat submitted search queries matching the particular distributionkeyword during the second period increased relative to the number ofusers that submitted search queries matching the particular distributionkeyword during the first period. Based on this determination, anincreasing trend score (e.g., a trend score signifying an increase insearch query volume) can be selected for the particular candidatedistribution keyword.

At least one of the candidate distribution parameters is suggested as anadditional distribution parameter (312). In some implementations, the atleast one distribution parameter is suggested based on the trend scores.For example, the magnitude of the trend scores for the candidatedistribution keywords can be compared to a trend threshold (e.g., aspecified value) to determine which of the trend scores meets the trendthreshold. Based on a determination that the magnitude of at least oneof the trend scores meets the trend threshold, the candidatedistribution keyword(s) corresponding to the at least one trend scorecan be suggested as additional distribution parameters.

In some implementations, the content sponsors to which the additionaldistribution parameters are suggested can be selected based on the twoor more content distribution campaigns that were identified as havingthe specified level of similarity. For example, one or more contentsponsors that are associated with the two or more content distributioncampaigns can be provided suggestion data suggesting the additionaldistribution parameters. In some implementations, at least one of thesuggested additional distribution parameters is a search query thatdiffers from any of the distribution parameters included in any of thesimilar content distribution campaigns. For example, the differentsuggested additional distribution parameter can be a search query thatonly matched a broad match distribution keyword in one of the similarcontent distribution campaigns.

FIG. 4 is block diagram of an example computer system 400 that can beused to perform operations described above. The system 400 includes aprocessor 410, a memory 420, a storage device 430, and an input/outputdevice 440. Each of the components 410, 420, 430, and 440 can beinterconnected, for example, using a system bus 450. The processor 410is capable of processing instructions for execution within the system400. In one implementation, the processor 410 is a single-threadedprocessor. In another implementation, the processor 410 is amulti-threaded processor. The processor 410 is capable of processinginstructions stored in the memory 420 or on the storage device 430.

The memory 420 stores information within the system 400. In oneimplementation, the memory 420 is a computer-readable medium. In oneimplementation, the memory 420 is a volatile memory unit. In anotherimplementation, the memory 420 is a non-volatile memory unit.

The storage device 430 is capable of providing mass storage for thesystem 400. In one implementation, the storage device 430 is acomputer-readable medium. In various different implementations, thestorage device 430 can include, for example, a hard disk device, anoptical disk device, a storage device that is shared over a network bymultiple computing devices (e.g., a cloud storage device), or some otherlarge capacity storage device.

The input/output device 440 provides input/output operations for thesystem 400. In one implementation, the input/output device 440 caninclude one or more of a network interface devices, e.g., an Ethernetcard, a serial communication device, e.g., and RS-232 port, and/or awireless interface device, e.g., and 802.11 card. In anotherimplementation, the input/output device can include driver devicesconfigured to receive input data and send output data to otherinput/output devices, e.g., keyboard, printer and display devices 460.Other implementations, however, can also be used, such as mobilecomputing devices, mobile communication devices, set-top box televisionclient devices, etc.

Although an example processing system has been described in FIG. 4,implementations of the subject matter and the functional operationsdescribed in this specification can be implemented in other types ofdigital electronic circuitry, or in computer software, firmware, orhardware, including the structures disclosed in this specification andtheir structural equivalents, or in combinations of one or more of them.

Embodiments of the subject matter and the operations described in thisspecification can be implemented in digital electronic circuitry, or incomputer software, firmware, or hardware, including the structuresdisclosed in this specification and their structural equivalents, or incombinations of one or more of them. Embodiments of the subject matterdescribed in this specification can be implemented as one or morecomputer programs, i.e., one or more modules of computer programinstructions, encoded on computer storage medium for execution by, or tocontrol the operation of, data processing apparatus. Alternatively or inaddition, the program instructions can be encoded on anartificially-generated propagated signal, e.g., a machine-generatedelectrical, optical, or electromagnetic signal, that is generated toencode information for transmission to suitable receiver apparatus forexecution by a data processing apparatus. A computer storage medium canbe, or be included in, a computer-readable storage device, acomputer-readable storage substrate, a random or serial access memoryarray or device, or a combination of one or more of them. Moreover,while a computer storage medium is not a propagated signal, a computerstorage medium can be a source or destination of computer programinstructions encoded in an artificially-generated propagated signal. Thecomputer storage medium can also be, or be included in, one or moreseparate physical components or media (e.g., multiple CDs, disks, orother storage devices).

The operations described in this specification can be implemented asoperations performed by a data processing apparatus on data stored onone or more computer-readable storage devices or received from othersources.

The term “data processing apparatus” encompasses all kinds of apparatus,devices, and machines for processing data, including by way of example aprogrammable processor, a computer, a system on a chip, or multipleones, or combinations, of the foregoing The apparatus can includespecial purpose logic circuitry, e.g., an FPGA (field programmable gatearray) or an ASIC (application-specific integrated circuit). Theapparatus can also include, in addition to hardware, code that createsan execution environment for the computer program in question, e.g.,code that constitutes processor firmware, a protocol stack, a databasemanagement system, an operating system, a cross-platform runtimeenvironment, a virtual machine, or a combination of one or more of them.The apparatus and execution environment can realize various differentcomputing model infrastructures, such as web services, distributedcomputing and grid computing infrastructures.

A computer program (also known as a program, software, softwareapplication, script, or code) can be written in any form of programminglanguage, including compiled or interpreted languages, declarative orprocedural languages, and it can be deployed in any form, including as astand-alone program or as a module, component, subroutine, object, orother unit suitable for use in a computing environment. A computerprogram may, but need not, correspond to a file in a file system. Aprogram can be stored in a portion of a file that holds other programsor data (e.g., one or more scripts stored in a markup languagedocument), in a single file dedicated to the program in question, or inmultiple coordinated files (e.g., files that store one or more modules,sub-programs, or portions of code). A computer program can be deployedto be executed on one computer or on multiple computers that are locatedat one site or distributed across multiple sites and interconnected by acommunication network.

The processes and logic flows described in this specification can beperformed by one or more programmable processors executing one or morecomputer programs to perform actions by operating on input data andgenerating output. The processes and logic flows can also be performedby, and apparatus can also be implemented as, special purpose logiccircuitry, e.g., an FPGA (field programmable gate array) or an ASIC(application-specific integrated circuit).

Processors suitable for the execution of a computer program include, byway of example, both general and special purpose microprocessors, andany one or more processors of any kind of digital computer. Generally, aprocessor will receive instructions and data from a read-only memory ora random access memory or both. The essential elements of a computer area processor for performing actions in accordance with instructions andone or more memory devices for storing instructions and data. Generally,a computer will also include, or be operatively coupled to receive datafrom or transfer data to, or both, one or more mass storage devices forstoring data, e.g., magnetic, magneto-optical disks, or optical disks.However, a computer need not have such devices. Moreover, a computer canbe embedded in another device, e.g., a mobile telephone, a personaldigital assistant (PDA), a mobile audio or video player, a game console,a Global Positioning System (GPS) receiver, or a portable storage device(e.g., a universal serial bus (USB) flash drive), to name just a few.Devices suitable for storing computer program instructions and datainclude all forms of non-volatile memory, media and memory devices,including by way of example semiconductor memory devices, e.g., EPROM,EEPROM, and flash memory devices; magnetic disks, e.g., internal harddisks or removable disks; magneto-optical disks; and CD-ROM and DVD-ROMdisks. The processor and the memory can be supplemented by, orincorporated in, special purpose logic circuitry.

To provide for interaction with a user, embodiments of the subjectmatter described in this specification can be implemented on a computerhaving a display device, e.g., a CRT (cathode ray tube) or LCD (liquidcrystal display) monitor, for displaying information to the user and akeyboard and a pointing device, e.g., a mouse or a trackball, by whichthe user can provide input to the computer. Other kinds of devices canbe used to provide for interaction with a user as well; for example,feedback provided to the user can be any form of sensory feedback, e.g.,visual feedback, auditory feedback, or tactile feedback; and input fromthe user can be received in any form, including acoustic, speech, ortactile input. In addition, a computer can interact with a user bysending documents to and receiving documents from a device that is usedby the user; for example, by sending web pages to a web browser on auser's client device in response to requests received from the webbrowser.

Embodiments of the subject matter described in this specification can beimplemented in a computing system that includes a back-end component,e.g., as a data server, or that includes a middleware component, e.g.,an application server, or that includes a front-end component, e.g., aclient computer having a graphical user interface or a Web browserthrough which a user can interact with an implementation of the subjectmatter described in this specification, or any combination of one ormore such back-end, middleware, or front-end components. The componentsof the system can be interconnected by any form or medium of digitaldata communication, e.g., a communication network. Examples ofcommunication networks include a local area network (“LAN”) and a widearea network (“WAN”), an inter-network (e.g., the Internet), andpeer-to-peer networks (e.g., ad hoc peer-to-peer networks).

The computing system can include clients and servers. A client andserver are generally remote from each other and typically interactthrough a communication network. The relationship of client and serverarises by virtue of computer programs running on the respectivecomputers and having a client-server relationship to each other. In someembodiments, a server transmits data (e.g., an HTML page) to a clientdevice (e.g., for purposes of displaying data to and receiving userinput from a user interacting with the client device). Data generated atthe client device (e.g., a result of the user interaction) can bereceived from the client device at the server.

While this specification contains many specific implementation details,these should not be construed as limitations on the scope of anyinventions or of what may be claimed, but rather as descriptions offeatures specific to particular embodiments of particular inventions.Certain features that are described in this specification in the contextof separate embodiments can also be implemented in combination in asingle embodiment. Conversely, various features that are described inthe context of a single embodiment can also be implemented in multipleembodiments separately or in any suitable subcombination. Moreover,although features may be described above as acting in certaincombinations and even initially claimed as such, one or more featuresfrom a claimed combination can in some cases be excised from thecombination, and the claimed combination may be directed to asubcombination or variation of a subcombination.

Similarly, while operations are depicted in the drawings in a particularorder, this should not be understood as requiring that such operationsbe performed in the particular order shown or in sequential order, orthat all illustrated operations be performed, to achieve desirableresults. In certain circumstances, multitasking and parallel processingmay be advantageous. Moreover, the separation of various systemcomponents in the embodiments described above should not be understoodas requiring such separation in all embodiments, and it should beunderstood that the described program components and systems cangenerally be integrated together in a single software product orpackaged into multiple software products.

Thus, particular embodiments of the subject matter have been described.Other embodiments are within the scope of the following claims. In somecases, the actions recited in the claims can be performed in a differentorder and still achieve desirable results. In addition, the processesdepicted in the accompanying figures do not necessarily require theparticular order shown, or sequential order, to achieve desirableresults. In certain implementations, multitasking and parallelprocessing may be advantageous.

What is claimed is:
 1. A method performed by one or more data processingapparatus, the method comprising: determining, by the one or more dataprocessing apparatus, a first number of users that submitted a givensearch query over a first time period; determining, by the one or moredata processing apparatus, a second number of users that submitted thegiven search query over a second time period that differs from the firsttime period and occurs after the first time period; determining, by theone or more data processing apparatus, a trend score for the givensearch query based on comparing the first number of users and the secondnumber of users; determining, by the one or more data processingapparatus, that the trend score is an increasing trend score based onthe second number being greater than the first number; identifying, bythe one or more data processing apparatus, a distribution parameter in afirst content distribution campaign that is matched by the given searchquery; identifying, by the one or more data processing apparatus, asecond content distribution campaign that has at least a specified levelof similarity with the first content distribution campaign, wherein thespecified level of similarity is a specified percentage of matchingdistribution parameters; determining that a difference between a numberof impressions allocated to each of the first and the second contentdistribution campaigns is at least as much as a specified differencevalue; determining, by the one or more data processing apparatus, thatthe second content distribution campaign does not include the givensearch query as a distribution parameter for content; providing, by theone or more data processing apparatus and based on the trend score andon the specified level of similarity between the first contentdistribution campaign and the second content distribution campaign,suggestion data suggesting the given search query as an additionaldistribution parameter for the second content distribution campaign,including providing, by the one or more data processing apparatus,suggestion data suggesting the particular distribution parameter to thesecond content distribution campaign based on the identification of thefirst and the second content distribution campaigns having at least thespecified level of similarity to each other.
 2. The method of claim 1,wherein identifying the first content distribution campaign and thesecond content distribution campaign comprises identifying at least onecontent distribution campaign in which a broad match distributionparameter differs from one or more of the search queries that match thebroad match distribution parameter.
 3. The method of claim 2, whereinproviding suggestion data suggesting the particular distributionparameter comprises providing suggestion data suggesting at least one ofthe one or more search queries that match the broad match distributionparameter as an additional distribution parameter for the second contentdistribution campaign.
 4. The method of claim 1, wherein the specifiedlevel of similarity includes at least one of a percentage of matchingdistribution parameters between the first content distribution campaignand the second content distribution campaign, an absolute number ofmatching distribution parameters between the first content distributioncampaign and the second content distribution campaign, and a specifieddifference value of a number of impressions allocated to the firstcontent distribution campaign and a number of impressions allocated tothe second content distribution campaign.
 5. The method of claim 1,comprising: identifying, by the one or more data processing apparatus, asecond distribution parameter in the second content distributioncampaign that is matched by the given search query; determining, by theone or more data processing apparatus, that the first contentdistribution campaign does not include the second distribution parameteras a distribution parameter for content; and providing, by the one ormore data processing apparatus and based on the trend score and on thespecified level of similarity between the first content distributioncampaign and the second content distribution campaign, suggestion datasuggesting the second distribution parameter as an additionaldistribution parameter for the first content distribution campaign. 6.The method of claim 1, comprising determining that a magnitude of thetrend score meets a trend threshold, wherein providing suggestion datasuggesting the particular distribution parameter as an additionaldistribution parameter comprises providing suggestion data suggestingthe particular distribution parameter as an additional distributionparameter based on the magnitude of the trend score meeting the trendthreshold.
 7. A non-transitory computer storage medium encoded with acomputer program, the program comprising instructions that when executedby one or more data processing apparatus cause the one or more dataprocessing apparatus to perform operations comprising: determining, bythe one or more data processing apparatus, a first number of users thatsubmitted a given search query over a first time period; determining, bythe one or more data processing apparatus, a second number of users thatsubmitted the given search query over a second time period that differsfrom the first time period and occurs after the first time period;determining, by the one or more data processing apparatus, a trend scorefor the given search query based on comparing the first number of usersand the second number of users; determining, by the one or more dataprocessing apparatus, that the trend score is an increasing trend scorebased on the second number being greater than the first number;identifying, by the one or more data processing apparatus, adistribution parameter in a first content distribution campaign that ismatched by the given search query; identifying, by the one or more dataprocessing apparatus, a second content distribution campaign that has atleast a specified level of similarity with the first contentdistribution campaign, wherein the specified level of similarity is aspecified percentage of matching distribution parameters; determiningthat a difference between a number of impressions allocated to each ofthe first and the second content distribution campaigns is at least asmuch as a specified difference value; determining, by the one or moredata processing apparatus, that the second content distribution campaigndoes not include the given search query as a distribution parameter forcontent; providing, by the one or more data processing apparatus andbased on the trend score and on the specified level of similaritybetween the first content distribution campaign and the second contentdistribution campaign, suggestion data suggesting the given search queryas an additional distribution parameter for the second contentdistribution campaign, including providing, by the one or more dataprocessing apparatus, suggestion data suggesting the particulardistribution parameter to the second content distribution campaign basedon the identification of the first and the second content distributioncampaigns having at least the specified level of similarity to eachother.
 8. The non-transitory computer storage medium of claim 7, whereinidentifying the first content distribution campaign and the secondcontent distribution campaign comprises identifying at least one contentdistribution campaign in which a broad match distribution parameterdiffers from one or more of the search queries that match the broadmatch distribution parameter.
 9. The non-transitory computer storagemedium of claim 8, wherein providing suggestion data suggesting theparticular distribution parameter comprises providing suggestion datasuggesting at least one of the one or more search queries that match thebroad match distribution parameter as an additional distributionparameter for the second content distribution campaign.
 10. Thenon-transitory computer storage medium of claim 7, wherein theinstructions cause the one or more data processing apparatus to performoperations comprising determining that a magnitude of the trend scoremeets a trend threshold, wherein providing suggestion data suggestingthe particular distribution parameter as an additional distributionparameter comprises providing suggestion data suggesting the particulardistribution parameter as an additional distribution parameter based onthe magnitude of the trend score meeting the trend threshold.
 11. Thenon-transitory computer storage medium of claim 7, wherein theinstructions cause the one or more data processing apparatus to performoperations comprising: identifying, by the one or more data processingapparatus, a second distribution parameter in the second contentdistribution campaign that is matched by the given search query;determining, by the one or more data processing apparatus, that thefirst content distribution campaign does not include the seconddistribution parameter as a distribution parameter for content; andproviding, by the one or more data processing apparatus and based on thetrend score and on the specified level of similarity between the firstcontent distribution campaign and the second content distributioncampaign, suggestion data suggesting the second distribution parameteras an additional distribution parameter for the first contentdistribution campaign.
 12. A system comprising: a data store storingsearch query data; one or more data processing apparatus that interactwith the data store and execute instructions that cause the one or moredata processing apparatus to perform operations including: determining,by the one or more data processing apparatus, a first number of usersthat submitted a given search query over a first time period;determining, by the one or more data processing apparatus, a secondnumber of users that submitted the given search query over a second timeperiod that differs from the first time period and occurs after thefirst time period; determining, by the one or more data processingapparatus, a trend score for the given search query based on comparingthe first number of users and the second number of users; determining,by the one or more data processing apparatus, that the trend score is anincreasing trend score based on the second number being greater than thefirst number; identifying, by the one or more data processing apparatus,a distribution parameter in a first content distribution campaign thatis matched by the given search query; identifying, by the one or moredata processing apparatus, a second content distribution campaign thathas at least a specified level of similarity with the first contentdistribution campaign, wherein the specified level of similarity is aspecified percentage of matching distribution parameters; anddetermining that a difference between a number of impressions allocatedto each of the first and the second content distribution campaigns is atleast as much as a specified difference value; determining, by the oneor more data processing apparatus, that the second content distributioncampaign does not include the given search query as a distributionparameter for content; providing, by the one or more data processingapparatus and based on the trend score and on the specified level ofsimilarity between the first content distribution campaign and thesecond content distribution campaign, suggestion data suggesting thegiven search query as an additional distribution parameter for thesecond content distribution campaign, including providing, by the one ormore data processing apparatus, suggestion data suggesting theparticular distribution parameter to the second content distributioncampaign based on the identification of the first and the second contentdistribution campaigns having at least the specified level of similarityto each other.
 13. The system of claim 12, wherein identifying the firstcontent distribution campaign and the second content distributioncampaign comprises identifying at least one content distributioncampaign in which a broad match distribution parameter differs from oneor more of the search queries that match the broad match distributionparameter.
 14. The system of claim 13, wherein providing suggestion datasuggesting the particular distribution parameter comprises providingsuggestion data suggesting at least one of the one or more searchqueries that match the broad match distribution parameter as anadditional distribution parameter for the second content distributioncampaign.
 15. The system of claim 12, wherein the instructions cause theone or more data processing apparatus to perform operations comprisingdetermining that a magnitude of the trend score meets a trend threshold,wherein providing suggestion data suggesting the particular distributionparameter as an additional distribution parameter comprises providingsuggestion data suggesting the particular distribution parameter as anadditional distribution parameter based on the magnitude of the trendscore meeting the trend threshold.
 16. The system of claim 12, whereinthe specified level of similarity includes at least one of a percentageof matching distribution parameters between the first contentdistribution campaign and the second content distribution campaign, anabsolute number of matching distribution parameters between the firstcontent distribution campaign and the second content distributioncampaign, and a specified difference value of a number of impressionsallocated to the first content distribution campaign and a number ofimpressions allocated to the second content distribution campaign. 17.The system of claim 12, wherein the instructions cause the one or moredata processing apparatus to perform operations comprising: identifying,by the one or more data processing apparatus, a second distributionparameter in the second content distribution campaign that is matched bythe given search query; determining, by the one or more data processingapparatus, that the first content distribution campaign does not includethe second distribution parameter as a distribution parameter forcontent; and providing, by the one or more data processing apparatus andbased on the trend score and on the specified level of similaritybetween the first content distribution campaign and the second contentdistribution campaign, suggestion data suggesting the seconddistribution parameter as an additional distribution parameter for thefirst content distribution campaign.